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ARXIV:2604.09532 · ROBUST VISION-LANGUAGE MODELS · SUBMITTED 13 APR · 20:33 UTC · FRESHNESS STALE
ARXIV:2604.09532ROBUST VISION-LANGUAGE MODELSSUBMITTED 13 APR · 20:33 UTCFRESHNESS STALEZibin Geng · Xuefeng Jiang · Jia Li · Zheng Li · Tian Wen · Lvhua Wu · +3 at arXiv
VisPrompt enhances vision-language models' robustness to label noise by injecting visual semantics into prompt learning, improving performance on noisy datasets.
Opportunity summary
Pain VisPrompt enhances vision-language models' robustness to label noise by injecting visual semantics into prompt learning, improving performance on noisy datasets.
Evidence 0 refs | 4 sources | 83% coverage
Blocker Evidence unverified
VisPrompt enhances vision-language models' robustness to label noise by injecting visual semantics into prompt learning, improving performance on noisy datasets. Visual content contains richer and more reliable semantic information, which remains more robust under…
Prompt learning is a parameter-efficient approach for vision-language models, yet its robustness under label noise is less investigated. Visual content contains richer and more reliable semantic information, which remains more robust under label noise.
ScienceToStartup currently rates this 6.0/10 on the public viability pass. This enables the prompt tokens to selectively aggregate visual information relevant to the current sample, thereby improving robustness by anchoring prompt learning to stable…
Robust Vision-Language Models moved forward this cycle; last verified April 2026. Public score 6.0/10. Implementation evidence is present through a linked repository.
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VisPrompt enhances vision-language models' robustness to label noise by injecting visual semantics into prompt learning, improving performance on noisy datasets.
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10.48550/arXiv.2604.09532VisPrompt enhances vision-language models' robustness to label noise by injecting visual semantics into prompt learning, improving performance on noisy datasets.
Abstract
Prompt learning is a parameter-efficient approach for vision-language models, yet its robustness under label noise is less investigated. Visual content contains richer and more reliable semantic information, which remains more robust under label noise. However, the prompt itself is highly susceptible to label noise. Motivated by this intuition, we propose VisPrompt, a lightweight and robust vision-guided prompt learning framework for noisy-label settings. Specifically, we exploit a cross-modal attention mechanism to reversely inject visual semantics into prompt representations. This enables the prompt tokens to selectively aggregate visual information relevant to the current sample, thereby improving robustness by anchoring prompt learning to stable instance-level visual evidence and reducing the influence of noisy supervision. To address the instability caused by using the same way of injecting visual information for all samples, despite differences in the quality of their visual cues, we further introduce a lightweight conditional modulation mechanism to adaptively control the strength of visual information injection, which strikes a more robust balance between text-side semantic priors and image-side instance evidence. The proposed framework effectively suppresses the noise-induced disturbances, reduce instability in prompt updates, and alleviate memorization of mislabeled samples. VisPrompt significantly improves robustness while keeping the pretrained VLM backbone frozen and introducing only a small amount of additional trainable parameters. Extensive experiments under synthetic and real-world label noise demonstrate that VisPrompt generally outperforms existing baselines on seven benchmark datasets and achieves stronger robustness. Our code is publicly available at https://github.com/gezbww/Vis_Prompt.
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PROBLEM
VisPrompt enhances vision-language models' robustness to label noise by injecting visual semantics into prompt learning, improving performance on noisy datasets. Visual content contains richer and more reliable semantic information, which remains more robust under label noise.
METHOD
Prompt learning is a parameter-efficient approach for vision-language models, yet its robustness under label noise is less investigated. Visual content contains richer and more reliable semantic information, which remains more robust under label noise.
RESULT
ScienceToStartup currently rates this 6.0/10 on the public viability pass. This enables the prompt tokens to selectively aggregate visual information relevant to the current sample, thereby improving robustness by anchoring prompt learning to stable instance-level visual evidenc...
WHY NOW
Robust Vision-Language Models moved forward this cycle; last verified April 2026. Public score 6.0/10. Implementation evidence is present through a linked repository.
Abstract-backed public claims while anchored extraction refreshes.
VisPrompt enhances vision-language models' robustness to label noise by injecting visual semantics into prompt learning, improving performance on noisy datasets. Visual content contains richer and more reliable semantic information, which remains more robust under label noise.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Prompt learning is a parameter-efficient approach for vision-language models, yet its robustness under label noise is less investigated. Visual content contains richer and more reliable semantic information, which remains more robust under label noise.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 6.0/10 on the public viability pass. This enables the prompt tokens to selectively aggregate visual information relevant to the current sample, thereby improving robustness by anchoring prompt learning to stable instance-level visual evidence and reducing the influence of noisy supervision. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Robust Vision-Language Models moved forward this cycle; last verified April 2026. Public score 6.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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VisPrompt enhances vision-language models' robustness to label noise by injecting visual semantics into prompt learning, improving performance on noisy datasets.
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Robust Vision-Language Models
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